Technical Field
The present invention relates generally to the field of query/answer systems in Natural Language Processing and, in particular, to the field of enhancing the Natural Language Processing for Question Answering systems.
Description of the Related Art
A Question Answering (QA) system builds on search engine technology to provide a single answer to a question posed to it in natural language. The QA system answers the natural language questions by querying data repositories and applying elements of language processing, information retrieval, and machine learning to arrive at a conclusion. Whereas a search engine uses document retrieval to return a list of results for a query, a QA system must find the one best answer. As used herein, a QA system refers to an online question answering system that uses Natural Language Processing (NLP) to read and understand the free-form questions presented to it. NLP falls under the realm of artificial intelligence (AI) and is gaining momentum in understanding the human language.
One particularly brilliant example of a QA system using NLP is “Watson”, a system designed by IBM on their DeepQA technology. Watson's extraordinary abilities were showcased on the quiz show “Jeopardy!” in 2011 when Watson beat two defending “Jeopardy!” champions. The manner in which QA systems such as Watson arrive at an answer is complex and involves hundreds of algorithms that return candidate answers. The candidate answers are “scored” to arrive at a presumed “best” answer. Then, the confidence level of that answer is determined. Adhering to the phenomenon known as “the wisdom of the crowds”, where the collective knowledge of the many is greater than one individual's knowledge, an answer will have a high confidence level if that answer was returned as the candidate answer by multiple algorithms.